• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Poisoning attack detection scheme based on data integrity sampling audit algorithm in neural network

    2023-12-05 07:36:40ZhaoNingningJiangRui

    Zhao Ningning Jiang Rui

    (School of Cyber Science and Engineering,Southeast University,Nanjing 210096,China)

    Abstract:To address the issue that most existing detection and defense methods can only detect known poisoning attacks but cannot defend against other types of poisoning attacks,a poisoning attack detecting scheme with data recovery (PAD-DR) is proposed to effectively detect the poisoning attack and recover the poisoned data in a neural network.First,the PAD-DR scheme can detect all types of poisoning attacks.The data sampling detection algorithm is combined with a real-time data detection method for input layer nodes using a neural network so that the system can ensure the integrity and availability of the training data to avoid being changed or corrupted.Second,the PAD-DR scheme can recover corrupted or poisoned training data from poisoning attacks.Cauchy Reed-Solomon (CRS) code technology can encode training data and store them separately.Once the poisoning attack is detected,the original training data is recovered,and the system may get data from any k nodes from all n stores to recover the original training data.Finally,the security objectives of the PAD-DR scheme to withstand poisoning attacks,resist forgery and tampering attacks,and recover the data accurately are formally proved.

    Key words:poisoning attack; neural network; deep learning; data integrity sampling audit

    Deep learning (DL)[1]is the foundation for several modern artificial intelligence applications.It has rapidly matured and entered safety-critical applications such as self-driving cars[2-3],drones,and robots[4-5].Deep neural network (DNN)[6]is a machine learning technique that tries to imitate the neurons of the human brain to transmit information and interpret data.

    Unfortunately,the defense methods against poisoning attacks are not systematic enough.Zhang et al.[14]designed a strong defense scheme called DUTI to deal with the label-flipping attack.Given a small portion of the trusted items,the DUTI scheme could learn the difference between the distribution of trusted items and training samples to find the potentially corrupted labels and then get the corrupted labels to a domain expert for further examination.To defend against tool command language (TCL) attacks[12],Peri et al.[15]proposed a scheme named Deep-kNN to remove poisoning samples.In Ref.[15],the authors compared the class labels of each testing sample with itskneighbors.If most neighbor samples differed from the testing sample,this testing sample should be removed.Using the cooperative deep learning system,Shen et al.[16]proposed a defense method called AUROR to defend against poisoning attacks.The AUROR scheme could automatically identify and display the process of abnormal distribution features and then detect malicious users in the system according to the abnormal features.Based on a prior observation that poisoned samples may exploit spare capacity in the neural network,Liu et al.[17]proposed a fine-pruning technology as a defense method to enhance the security of deep neural networks by eliminating some dormant neurons to turn off poisoning behaviors.

    Considering that the previous schemes[14-17]can only defend against specific attacks,Diakonikolas et al.[18]proposed a robust algorithm named Server that could defend against poisoning attacks in classification and regression models.Chen et al.[19]proposed a generic and attack-agnostic defense approach called De-Pois.The key idea of De-Pois was to train a mimic model to imitate the behavior of the target model with clean samples.

    In this study,we proposed a poisoning attack-detecting scheme with data recovery (PAD-DR).First,we designed a data sampling audit algorithm and combined it with a real-time data detection method to detect all kinds of poisoning attacks.Second,we applied Cauchy Reed-Solomon (CRS) code technology to encode training data and store them on multiple servers to recover the corrupted training data.Finally,we formally proved the security goals of our PAD-DR scheme to withstand poisoning attacks and to recover the data accurately.

    1 Preliminaries

    1.1 Bilinear mapping

    Definition1LetG1andG2be multiplicative cyclic groups of a large prime orderp; a pairing is a bilinear mape:G1×G1→G2.It satisfies the following properties:

    1) Bilinear.e(ua,vb)=e(u,v)ab,?u,v∈G1;a,b∈Zp.

    2) Non-degeneracy.?u,v∈G1,thuse(u,v)≠1∈G1.

    3) Computability.?u,v∈G1; there is a polynomial time algorithm to calculatee(u,v).

    4) Safety.It is difficult to calculate the discrete logarithm problem inG1andG2.

    1.2 Discrete logarithm problem (DLP)

    1.3 Computational Diffie-Hellman problem

    1.4 Co-computational bilinear Diffie-Hellman problem (CO-CDH)

    2 Proposed PAD-DR Scheme

    2.1 System model

    In our PAD-DR scheme,as shown in Fig.1,the system consists of five types of entities: system administrator (SA),third party auditor (TPA),coded data stores (CDSi),training data store (TDS),and nodes of the neural network for input layer (NNs).SA is responsible for processing the original training data.TPA is responsible for generating challenges to detect whether poisoning attacks are launched.

    Fig.1 System model for Our PAD-DR scheme

    CDSiare cloud spaces for storing regenerating backup data,where the regenerating backup data are distributed onnmultiple CDSi.TDS stores the original training data with tags and sends them to the NNs.NNs receive the training data and feed them to the neural network.Besides,NNs are responsible for detecting the poisoning attacks in real-time with the data tags.

    2.2 Threat model

    Our PAD-DR scheme defines the threat model in terms of the SA,TPA CDSi,TDS,NNs,and network attackers (NAs).

    The SA is credible.The SA encodes the training data,generates tags for the original data,and collects regenerating data blocks to help recover the poisoned data.

    The TPA is semitrusted.The TPA may run the algorithm and protocol in the system.However,the TPA is curious about the contents of training data and tries to obtain the contents of training data in the verification process.

    The TDS is semitrusted.The TDS may faithfully run the algorithm and protocol in the system.However,to maintain the reputation,they may conceal the truth when poisoning samples attack the training data.At that time,TDS may attempt to forge proofs to cheat TPA for passing the auditing process.CDSiare credible and may store encoded regenerating data for backup.NNs are credible and may run the algorithm and protocol in the system and transmit the training data to the nodes of the next layer.

    NAs are malicious.NAs attempt to launch poisoning sample attacks.Furthermore,to pass the audit by TPA and NNs,NAs may attempt to launch tempering and forgery attacks by forging data and signature proofs.

    3 Construction of PAD-DR Scheme

    We proposed the detailed construction of our PAD-DR scheme.The scheme includes three phases: the setup,detection and recovery phases.Details of each phase are as follows.

    3.1 Setup phase

    The setup phase includes setup,encoding and SigGen algorithms.Among them,the setup algorithm generates system parameters,the encoding algorithm applies CRS code technology to encode the original training data for backup,and the SigGen algorithm generates tags for the original training data.Three algorithms are described in detail as follows.

    3.1.1 Setup algorithm

    3.1.2 Encoding algorithm

    Supposem,k,w∈Z+,a Galois field GF(2w),for data fileF={m1,m2,…,mk},SA utilizes CRS[22]technology to generaten=m+kencoded data blocks by constructing a code matrixC=ΨM,where the encoding matrixΨisn×k,and the message matrixMisk×1.

    Then,the encoding matrixΨis designed as follows:

    Hence,thei-th row ofΨis defined as the encoding vectorΨi(i∈{1,2,…,n}).

    For example,letk=6,m=4,andn=10,the Cauchy matrixGis with dimensions 4×6 on GF(28).LetX={0,1,2,3,4,5},Y={6,7,8,9},the encoding process is shown as follows:

    C=Encode(M)=ΨM=

    3.1.3 SigGen

    3.2 Detection phase

    The detection phase consists of TPA detection and node detection algorithms.With our proposed sampling audit algorithm,the TPA detection algorithm can detect poisoning attacks on the training data stored in TDS.In real-time,a node detection algorithm can detect poisoning attacks on the training data at the input layer for NNs.

    3.2.1 TPA detection

    Having received the response proof from the TDS,the TPA verifies the proof as follows.TPA checks the verification equation as

    (1)

    If Eq.(1) holds,the training data stored on TDS are securely protected.Otherwise,the training data stored on TDS should be changed or corrupted by the poisoning attacks.When the training data is detected to be poisoned,the TPA immediately makes an alarm for the poisoning attack and sends feedback {error} to the SA.Then,the SA performs the recovery operation to recover the poisoned data on TDS.

    3.2.2 Node detection

    After receiving training data with signatures from TDS,NNs run a node detection algorithm to detect poisoning attacks as follows.First,NNs verify the correctness of the signatureStsk(F‖μ′‖σ′) by TDS’s public key according to the RSA signature algorithm.If the verification fails,the NNs abort the message and send {error} to SA.Otherwise,we have to design the real-time data detection method for the NNs to check the verification equation as follows:

    (2)

    If Eq.(2) holds,the training data sent by TDS are complete and correct.Otherwise,the poisoning attacks could change or corrupt the training data.NNs may send error feedback to inform SA that the training data could be attacked and request SA to recover the training data.

    3.3 Recovery phase

    In the recovery phase,there is a data recovery algorithm.Once SA receives error feedback from TPA or NNs,implying that the training data has been poisoned and attacked,SA may execute the data recovery algorithm to recover the original training data.

    With the data recovery algorithm,the original data fileF={m1,m2,…,mk} can be recovered by collecting anyknodes of CDSifor itsciandΨi.Thus,the original data fileFcan be recovered through linear operations on the entries of anykrows of the code matrixC.

    Next,based on the definition in the preliminaries,we provided a theorem indicating that the entire process of the PAD-DR scheme is correct.

    Theorem1Our PAD-DR scheme can correctly run to detect the poisoning attack and recover the original training data accurately.

    4 Security Analysis

    In this section,we formally proved that our PAD-DR scheme can resist tampering and forgery attacks.Also,we proved that the poisoned data can be correctly recovered.

    4.1 Related theorems

    Some theorems formally showed that our PAD-DR scheme can resist tampering and forgery attacks.Furthermore,we showed that the poisoned data can be correctly recovered.

    Theorem2In our PAD-DR scheme,it is computationally infeasible for the TDS and NAs to forge proof for passing the TPA’s auditing.Suppose there is a (t,?)-algorithm to forge a proof.Then,if a (t′,?′) adversary can solve the Co-CDH problem witht′≤t+qcG1and ?′=?/e(1+qs),whereqcG1denotes one exponentiation time inG1,andqsdenotes the number of requests.

    Theorem3In our PAD-DR scheme,NNs can detect poisoned samples in real-time training data received from TDS.

    Theorem4In our PAD-DR scheme,the original data fileFcan be recovered by collectingciandΨifrom anyknodes of CDSi.

    4.2 Security goals comparison

    In this section,the security goals of our PAD-DR scheme are compared with that of DUTI[14],Deep-kNN[15],Server[18]and De-Pois[19].The comparison includes specific poisoning attack detection,arbitrary poisoning attack detection,and poisoned data recovery.In Tab.1,“√” depicts that the security problem has been solved,“×” indicates that the problem has not been solved.

    Tab.1 Security goals comparison

    According to Tab.1,our PAD-DR scheme can realize all the security goals mentioned above,while other schemes cannot.

    5 Experiment and Performance Analysis

    This section begins by describing the experimental setup and the accuracy under different attacks.Then,we analyzed the communication overhead and computation costs in detecting poisoning attacks of training data and evaluated the performance of our PAD-DR scheme.

    5.1 Experiment setup

    5.1.1 Datasets

    The training data used in our scheme are from MNIST,CIFAR-10,and house pricing.MNIST is a database of handwritten digits containing 60 000 training sample sets and 10 000 test sample sets stored in binary form.The width and height of each sample image are 28×28.The CIFAR-10 database contains 32×32 color images in 10 different classes,with 50 000 training and 10 000 testing images.The 10 different classes include cars,dogs,frogs,airplanes,cars,ships,horses,birds,and trucks.The house pricing dataset utilizes predictor variables such as the number of bedrooms and lot square footage.It contains 1 460 houses and 81 features.In the experiment,we randomly split this dataset into training and testing datasets with 70% and 30% of the data,respectively.

    5.1.2 Experimental environment and parameter setting

    We evaluated the process of detecting poisoning samples on NVIDIA 2080Ti GPU and applied Ali Cloud to store training data.The operating system is Windows 10.The RAM size is 8 GB.Matrix multiplication algorithms are implemented with the open-source library Jerasure Version (1.2).Auditing algorithms are implemented onCwith a pairing-based cryptography library.The curve utilized in the experiment is an MNT curve.We set the length ofG1to 175.In our experiment,we adopted a common neural network that applies three full connection layers with ReLU activation and one full connection layer with sigmoid activation,and we applied the cross-entropy loss function to calculate its loss.

    5.1.3 Generation of poisoning training data

    To verify the performance of our PAD-DR scheme,we randomly extracted 10%,15%,20%,25%,and 30% of the training data to generate poisoning samples.To compare the detection rate of poisoned samples for our PAD-DR scheme,TCL[12],LF[8],and R[13]attacks are used to generate poisoning data.For MNIST,the number of iterations is 400,the learning rate is set to 0.1,and the initial value is set to 0.01.For CIFAR-10,the number of iterations is 6 000,the learning rate is 0.01,and the initial value is 0.01.

    5.2 Detection rate for poisoning attacks

    In this section,we evaluated the detection rate of poisoned samples for our PAD-DR scheme compared with that of TRIM[13],DUTI[14],Deep-kNN[15],Server[18],and De-Pois[19].The results are presented in Figs.2,3,and 4,respectively.

    As shown in Fig.2,for TCL attacks,the detection rate of our PAD-DR scheme is always 100%,which is higher than that of Refs.[15,19].There are two reasons for the relatively low detection rate of Ref.[15].One is that the authors only compared the samples with the surroundingksamples,which was not sufficient or accurate.The other reason is that the authors should set an artificial setting threshold to distinguish poisoned samples from clean samples.The main reason for the relatively low detection rate of Ref.[19]is that the authors adopted conditional GAN technology to expand a small part of a trusted dataset as the whole training data,which could not fully reflect the features of the real training data.In our PAD-DR scheme,we could only detect any changed or poisoned samples in the training data by verifying Eqs.(1) and (2).Hence,the detection rate of our PAD-DR scheme is always 100%.

    Fig.2 Detection rate under TCL attacks

    As illustrated in Fig.3,for TF attacks,the detection rate of our PAD-DR scheme is always 100%,which is higher than that of Refs.[14,18-19].In Ref.[14],the main reason for the relatively low detection rate is that the authors required a small piece of completely reliable data,which could not be ensured in the system to detect the TF attack.In Ref.[18],the detection rate decreased rapidly with the increase of the poisoning rate.The main reason to detect the TF attack is that the authors should set an artificial setting threshold,which could not accurately distinguish poisoned samples from clean samples.In Ref.[19],the main reason for the relatively low detection rate is that the authors should train the mimic model,which was ineffective in obtaining accurate prediction results.

    Fig.3 Detection rate under LF attacks

    As depicted in Fig.4,for R attacks,the detection rate of our PAD-DR scheme is always 100%,which is higher than that of Refs.[13-14,19].With the increase of the poisoning rate,the detection rate of Refs.[13-14,19]decreased rapidly.In Ref.[13],to detect R attacks,the authors ignored the influence of poisoned samples in the lowest residual set with iteration,which may cause the failure of R-attack detection.In Ref.[14],the authors required a small piece of completely reliable data to detect an R attack,which could not be ensured in the system.In Ref.[19],the authors should train the mimic model to detect R attacks,which was not effective enough to obtain accurate prediction results.In our PAD-DR scheme,we could only detect any changed or poisoned samples in the training data using verifying Eqs.(1) and (2).Hence,the detection rate of our PAD-DR scheme for R attacks is always 100%.

    Fig.4 Detection rate under R attacks

    5.3 Computation cost

    We evaluated the computation cost for our PAD-DR scheme,whereLdenotes the length of each encoded data block,EGdenotes one exponentiation inG1andG2,EZdenotes one exponentiation inZp,MGindicates a multiplication operation on groupsG1andG2,MZindicates a multiplication operation on the number fieldZp,MFrepresents a matrix multiplication in a finite fieldF(2n),MIrepresents the cost of computing the inverse of the matrixM,AZindicates an addition operation on the number fieldZp,Pedenotes the computation cost of one pairing operatione,andHdenotes the computation cost of an operation of calculating the hash value for a number.Furthermore,|I|represents the number of challenged data blocks.All the statistical results are the averages of 20 trials.

    Tab.2 depicts the computation cost of our PAD-DR scheme in the setup,detection,and recovery phases.

    Tab.2 Computation cost of our PAD-DR scheme

    In the detection phase,the computation cost refers to two parts: TPA detection and node detection.The computation cost of TPA detection is|I|(MZ+H+EG)+(|I|-1)(AZ+MG)+Pe,where|I|(MZ+H)+(|I|-1)AZand|I|EG+(|I|-1)MGare the computation cost of TDS to generate a linear combination of data blocks and an aggregated tag,respectively,wherePeis the computation cost of TPA to verify the proof.The computation cost of node detection is|I|(MZ+H+EG)+(|I|-1)(AZ+MG)+Pe,where|I|(MZ+H)+(|I|-1)AZand|I|EG+(|I|-1)MGare the computation cost of TDS to generate a linear combination of data blocks and an aggregated tag,respectively,Peis the computation cost of NNs to verify the proof.Hence,the computation cost in the detection phase is 2|I|(MZ+H+EG)+2(|I|-1)(AZ+MG)+2P.

    5.4 Communication overhead

    We assessed the communication overhead in the setup,detection,and recovery phases.In this section,|Zp|represents the size ofZp,|G|represents the size of groupG,and|GF|represents the size ofGF(2w).Tab.3 presents the communication overhead for the three phases.

    Tab.3 Communication overhead for our PAD-DR scheme

    In the setup phase,communication overhead mainly includes two parts.The one is SA uploads data and tags {F,Φ} to TDS.The other is SA sends the encoded data {ci}i=1,2,…,nto CDSi.Hence,the communication overhead in the setup phase is sizeof(F)+k|G|+n|GF|.

    In the detection phase,the communication overhead mainly refers to two parts: TPA detection and node detection.The communication overhead for TPA detection includes two parts.One is that TPA sends a challenge chal={(i,vi)} to TDS.The other is that TDS replies as proofP={μ,σ}.Hence,the communication overhead for TPA detection is 2|I||Zp|and|G|+|Zp|.The communication overhead for node detection is sizeof(F)+|G|+(k+1)|Zp|which arises from TDS sending {F,μ′,σ′,Sssk(F‖μ′‖σ′),vi}i∈Ito NNs.Thus,the total communication overhead in the detection phase is sizeof(F)+2|G|+(k+2+2|I|)|Zp|.

    In the recovery phase,the communication overhead is decided by the data size of {ci,Ψi},which arises from theknodes of CDSi.Hence,the communication overhead of our PAD-DR scheme is 2k|GF|.

    6 Conclusions

    1) An algorithm combining data sampling audit and real-time data detection is designed,and experiments show that the algorithm can accurately detect toxic data contained in the data.

    2) A data recovery algorithm based on CRS encoding is designed,and experiments show that it can efficiently restore poisoned data to clean data.

    3) The current algorithm cannot guarantee the security of parameters during transmission.We will conduct further research on improving the integrity and confidentiality of parameters in the future.

    天堂中文最新版在线下载| 校园人妻丝袜中文字幕| 男女下面进入的视频免费午夜| 91aial.com中文字幕在线观看| av播播在线观看一区| 国产午夜精品久久久久久一区二区三区| av免费观看日本| 天天躁夜夜躁狠狠久久av| 亚洲欧美一区二区三区黑人 | 我的女老师完整版在线观看| 亚洲精品久久久久久婷婷小说| 国产精品.久久久| 国产精品一区二区三区四区免费观看| 纵有疾风起免费观看全集完整版| 久久久午夜欧美精品| 国产精品一及| 最近最新中文字幕免费大全7| 精品一区在线观看国产| 久久6这里有精品| 国产精品久久久久成人av| 99热6这里只有精品| 男人狂女人下面高潮的视频| 精品99又大又爽又粗少妇毛片| 建设人人有责人人尽责人人享有的 | 精品国产乱码久久久久久小说| 国产精品国产三级国产av玫瑰| 狂野欧美激情性xxxx在线观看| av国产精品久久久久影院| 青春草国产在线视频| 美女高潮的动态| 成年av动漫网址| 精品亚洲成a人片在线观看 | 一级a做视频免费观看| 国产精品av视频在线免费观看| 亚洲第一区二区三区不卡| 国产在线免费精品| 亚洲久久久国产精品| 久久这里有精品视频免费| 国产深夜福利视频在线观看| 国产中年淑女户外野战色| 国精品久久久久久国模美| av一本久久久久| 国产精品一及| 国产 一区精品| 日韩欧美 国产精品| 成人亚洲精品一区在线观看 | 久久热精品热| av天堂中文字幕网| 99国产精品免费福利视频| 小蜜桃在线观看免费完整版高清| 久久精品国产亚洲av天美| 亚洲精品成人av观看孕妇| 亚洲国产色片| 热99国产精品久久久久久7| 成人影院久久| 99久久精品热视频| 色吧在线观看| 爱豆传媒免费全集在线观看| 国产精品爽爽va在线观看网站| videossex国产| 在线免费十八禁| 国产亚洲精品久久久com| 91精品国产国语对白视频| 欧美性感艳星| 国产亚洲欧美精品永久| 精品一区二区免费观看| 美女主播在线视频| 高清日韩中文字幕在线| 国产亚洲最大av| 啦啦啦视频在线资源免费观看| 成人无遮挡网站| 热99国产精品久久久久久7| 最近的中文字幕免费完整| 亚洲图色成人| 欧美一级a爱片免费观看看| 一级爰片在线观看| 欧美97在线视频| 国产色婷婷99| 亚洲国产毛片av蜜桃av| 久久久欧美国产精品| 小蜜桃在线观看免费完整版高清| 国产伦在线观看视频一区| 夫妻性生交免费视频一级片| 日韩人妻高清精品专区| 日本午夜av视频| 亚洲精品乱久久久久久| 免费av中文字幕在线| 久久 成人 亚洲| 国产 精品1| 国产精品国产三级国产专区5o| 丝袜脚勾引网站| 中文乱码字字幕精品一区二区三区| 欧美xxxx黑人xx丫x性爽| 免费观看性生交大片5| 在线观看美女被高潮喷水网站| 18禁在线无遮挡免费观看视频| 国产av国产精品国产| 超碰av人人做人人爽久久| 亚洲精品国产成人久久av| 国产成人精品婷婷| 日韩中文字幕视频在线看片 | 在线免费观看不下载黄p国产| 国产亚洲精品久久久com| 亚洲婷婷狠狠爱综合网| 亚洲aⅴ乱码一区二区在线播放| 麻豆成人午夜福利视频| 人妻系列 视频| 欧美3d第一页| 青春草国产在线视频| 久久99热6这里只有精品| 精品亚洲成a人片在线观看 | h视频一区二区三区| 婷婷色av中文字幕| 日韩一区二区三区影片| 乱系列少妇在线播放| 老司机影院成人| 最近手机中文字幕大全| 99久久综合免费| 亚洲高清免费不卡视频| 久久国产亚洲av麻豆专区| 日本av手机在线免费观看| 超碰av人人做人人爽久久| 搡老乐熟女国产| 99九九线精品视频在线观看视频| 欧美少妇被猛烈插入视频| 九色成人免费人妻av| 国产成人精品婷婷| 自拍欧美九色日韩亚洲蝌蚪91 | 亚洲精品一区蜜桃| 久久久久久人妻| 国产久久久一区二区三区| 欧美成人午夜免费资源| 亚洲不卡免费看| 丝瓜视频免费看黄片| 精品99又大又爽又粗少妇毛片| 久久久久久久国产电影| 欧美 日韩 精品 国产| 国产精品蜜桃在线观看| 国产精品伦人一区二区| 男人狂女人下面高潮的视频| 国产精品伦人一区二区| 国产精品久久久久久久久免| 亚洲成人一二三区av| 国产精品久久久久久精品古装| 超碰97精品在线观看| 干丝袜人妻中文字幕| 超碰97精品在线观看| 欧美成人精品欧美一级黄| 中文字幕亚洲精品专区| 欧美日韩亚洲高清精品| 在线亚洲精品国产二区图片欧美 | 亚洲精品日韩在线中文字幕| 亚洲激情五月婷婷啪啪| freevideosex欧美| 免费高清在线观看视频在线观看| 在线观看免费日韩欧美大片 | 国产中年淑女户外野战色| 国产精品国产三级国产专区5o| 欧美丝袜亚洲另类| 狂野欧美白嫩少妇大欣赏| 99热全是精品| 男人狂女人下面高潮的视频| 中文字幕制服av| 中文在线观看免费www的网站| 国产一区有黄有色的免费视频| 国产色婷婷99| 国产精品人妻久久久久久| 国产精品99久久久久久久久| 不卡视频在线观看欧美| www.av在线官网国产| 成人亚洲欧美一区二区av| 欧美区成人在线视频| 日本黄大片高清| 精品人妻偷拍中文字幕| 亚洲精品自拍成人| 国产精品无大码| 少妇丰满av| 又大又黄又爽视频免费| 18禁裸乳无遮挡动漫免费视频| 国产国拍精品亚洲av在线观看| 国产男女内射视频| 免费观看性生交大片5| 国产av国产精品国产| 欧美xxxx黑人xx丫x性爽| 日本欧美国产在线视频| 久久久久网色| 高清欧美精品videossex| 男女边吃奶边做爰视频| 国产精品成人在线| 波野结衣二区三区在线| 国产淫语在线视频| 国产69精品久久久久777片| 欧美xxxx黑人xx丫x性爽| 久久ye,这里只有精品| 久久人人爽av亚洲精品天堂 | 国产精品蜜桃在线观看| 亚洲精品乱码久久久v下载方式| 久久久久久久国产电影| 九九爱精品视频在线观看| 久久久久国产精品人妻一区二区| 欧美3d第一页| 99久久人妻综合| 不卡视频在线观看欧美| 成人黄色视频免费在线看| 亚洲国产av新网站| 我要看日韩黄色一级片| 插阴视频在线观看视频| 亚洲欧美日韩另类电影网站 | 日韩欧美一区视频在线观看 | 中文资源天堂在线| 免费看光身美女| 国产精品久久久久久久电影| 亚洲精品亚洲一区二区| 99热国产这里只有精品6| 日韩av免费高清视频| 天堂8中文在线网| 夜夜骑夜夜射夜夜干| 久久精品国产亚洲网站| 高清毛片免费看| 久热这里只有精品99| .国产精品久久| 日韩av不卡免费在线播放| 又大又黄又爽视频免费| 国产男人的电影天堂91| 午夜免费鲁丝| 亚洲四区av| 国产亚洲av片在线观看秒播厂| 亚洲经典国产精华液单| 亚洲一级一片aⅴ在线观看| 日本爱情动作片www.在线观看| 18禁裸乳无遮挡免费网站照片| 99热6这里只有精品| 精品亚洲乱码少妇综合久久| 久久精品熟女亚洲av麻豆精品| 十八禁网站网址无遮挡 | 黄色配什么色好看| 成人毛片60女人毛片免费| 五月伊人婷婷丁香| 久久久国产一区二区| 一区二区三区四区激情视频| 中文字幕精品免费在线观看视频 | 韩国av在线不卡| 日日啪夜夜爽| 在线播放无遮挡| 一级毛片黄色毛片免费观看视频| 久久99蜜桃精品久久| 各种免费的搞黄视频| 欧美一区二区亚洲| 日本一二三区视频观看| 亚洲精品456在线播放app| 亚洲三级黄色毛片| 国产成人精品福利久久| 午夜福利高清视频| 99久久精品热视频| 天堂8中文在线网| 日韩亚洲欧美综合| 欧美高清性xxxxhd video| 久久国产精品男人的天堂亚洲 | 亚洲人成网站高清观看| av视频免费观看在线观看| 女性生殖器流出的白浆| 亚洲精品亚洲一区二区| 一本—道久久a久久精品蜜桃钙片| 中国国产av一级| 老司机影院成人| 高清毛片免费看| 尾随美女入室| 边亲边吃奶的免费视频| 国产伦精品一区二区三区视频9| 日韩强制内射视频| 日本一二三区视频观看| 国产v大片淫在线免费观看| 美女中出高潮动态图| 国产成人精品福利久久| 亚洲精品自拍成人| 大码成人一级视频| 99久久综合免费| 国产69精品久久久久777片| 成人一区二区视频在线观看| 久久国产精品大桥未久av | 永久免费av网站大全| 国产成人freesex在线| 国产免费一级a男人的天堂| 免费播放大片免费观看视频在线观看| 精品一区在线观看国产| 中文字幕精品免费在线观看视频 | 国产精品伦人一区二区| 久久久精品免费免费高清| 欧美最新免费一区二区三区| 亚洲av中文av极速乱| 国产精品人妻久久久影院| 成人无遮挡网站| 成年av动漫网址| 99re6热这里在线精品视频| 亚洲激情五月婷婷啪啪| 久久久久视频综合| 国产成人免费无遮挡视频| 热re99久久精品国产66热6| 久久精品熟女亚洲av麻豆精品| 大陆偷拍与自拍| 99热国产这里只有精品6| 黄色怎么调成土黄色| 亚洲欧美日韩另类电影网站 | 久久久久国产网址| 国产极品天堂在线| 夫妻午夜视频| 女性被躁到高潮视频| 乱码一卡2卡4卡精品| 国产在线免费精品| av免费观看日本| 深爱激情五月婷婷| 下体分泌物呈黄色| 观看免费一级毛片| 网址你懂的国产日韩在线| 街头女战士在线观看网站| 色哟哟·www| 国产片特级美女逼逼视频| 97在线人人人人妻| 三级经典国产精品| 黄片无遮挡物在线观看| 激情五月婷婷亚洲| 乱系列少妇在线播放| 欧美97在线视频| 美女脱内裤让男人舔精品视频| 免费播放大片免费观看视频在线观看| 久久综合国产亚洲精品| 99热全是精品| 国产又色又爽无遮挡免| 男女啪啪激烈高潮av片| 欧美精品国产亚洲| 亚洲欧美清纯卡通| 国产在线一区二区三区精| 国产白丝娇喘喷水9色精品| 深夜a级毛片| 国产精品精品国产色婷婷| 亚洲精品成人av观看孕妇| 日韩欧美一区视频在线观看 | 欧美日韩综合久久久久久| 黄片wwwwww| 国产av国产精品国产| 美女视频免费永久观看网站| 久久av网站| 久久99蜜桃精品久久| 欧美性感艳星| a级毛色黄片| 日韩成人伦理影院| 日本与韩国留学比较| 超碰av人人做人人爽久久| 国产精品一二三区在线看| 国产精品不卡视频一区二区| 亚洲激情五月婷婷啪啪| 欧美另类一区| 秋霞在线观看毛片| 欧美老熟妇乱子伦牲交| 久久99精品国语久久久| 狂野欧美激情性xxxx在线观看| 久久久久性生活片| 蜜桃在线观看..| 国产亚洲5aaaaa淫片| 国产精品女同一区二区软件| 99热全是精品| 国产伦理片在线播放av一区| 日日啪夜夜撸| 免费大片18禁| 久热久热在线精品观看| 一个人看视频在线观看www免费| av国产免费在线观看| 国精品久久久久久国模美| 久热久热在线精品观看| 九草在线视频观看| 成人国产麻豆网| 亚洲国产最新在线播放| 国产大屁股一区二区在线视频| 欧美日韩综合久久久久久| 99久久中文字幕三级久久日本| 一级毛片我不卡| 国产色婷婷99| 国内少妇人妻偷人精品xxx网站| 日本猛色少妇xxxxx猛交久久| 少妇人妻精品综合一区二区| 欧美高清成人免费视频www| freevideosex欧美| 国产精品女同一区二区软件| 国产精品伦人一区二区| 色综合色国产| 一级毛片 在线播放| 在现免费观看毛片| 国产成人freesex在线| 亚洲怡红院男人天堂| 春色校园在线视频观看| 日韩电影二区| 九九久久精品国产亚洲av麻豆| 久久99热6这里只有精品| 在线看a的网站| 欧美成人一区二区免费高清观看| 亚洲性久久影院| 777米奇影视久久| 久久久久久久国产电影| 在线观看av片永久免费下载| 能在线免费看毛片的网站| 夜夜爽夜夜爽视频| 最新中文字幕久久久久| 51国产日韩欧美| av在线蜜桃| 亚洲色图综合在线观看| 少妇的逼水好多| 高清日韩中文字幕在线| 热re99久久精品国产66热6| 日韩欧美 国产精品| 少妇猛男粗大的猛烈进出视频| 成人午夜精彩视频在线观看| kizo精华| 国产乱来视频区| 在线播放无遮挡| 亚洲av成人精品一二三区| 日韩欧美精品免费久久| 国产成人a区在线观看| 日本av免费视频播放| 亚洲综合精品二区| 欧美激情极品国产一区二区三区 | 超碰av人人做人人爽久久| 亚洲三级黄色毛片| 精品国产三级普通话版| 99热全是精品| 三级国产精品欧美在线观看| 午夜老司机福利剧场| 久久精品国产亚洲av涩爱| 亚洲一级一片aⅴ在线观看| 欧美成人午夜免费资源| 国产精品福利在线免费观看| 日韩欧美精品免费久久| 午夜免费观看性视频| 黄片无遮挡物在线观看| 丰满少妇做爰视频| 91狼人影院| 久久久精品94久久精品| 亚洲国产毛片av蜜桃av| 99热网站在线观看| 丝瓜视频免费看黄片| 只有这里有精品99| 久久久久网色| 国产免费福利视频在线观看| 婷婷色综合www| 久久女婷五月综合色啪小说| 国产高清有码在线观看视频| 国产精品人妻久久久久久| 久久久久久伊人网av| 久久久久久久国产电影| 日日啪夜夜撸| 啦啦啦视频在线资源免费观看| 久久精品熟女亚洲av麻豆精品| 久久久久久久久大av| 亚洲不卡免费看| 日韩不卡一区二区三区视频在线| 18禁动态无遮挡网站| 久久久午夜欧美精品| 五月天丁香电影| 免费观看av网站的网址| 色视频在线一区二区三区| 丝袜喷水一区| 久久99精品国语久久久| 国产亚洲精品久久久com| 少妇高潮的动态图| 国产精品免费大片| 熟妇人妻不卡中文字幕| 精品人妻偷拍中文字幕| 欧美xxxx黑人xx丫x性爽| 久久 成人 亚洲| 美女高潮的动态| 精品国产一区二区三区久久久樱花 | 久久婷婷青草| 我的女老师完整版在线观看| 精品99又大又爽又粗少妇毛片| 亚洲精品,欧美精品| 亚洲欧美中文字幕日韩二区| 色网站视频免费| 美女内射精品一级片tv| 久久久久久久久久久免费av| 在线观看美女被高潮喷水网站| 国产 一区 欧美 日韩| 91aial.com中文字幕在线观看| 亚洲精品国产av成人精品| 国产成人午夜福利电影在线观看| 少妇丰满av| 国产中年淑女户外野战色| 欧美日韩综合久久久久久| 搡老乐熟女国产| 国产高清三级在线| 看非洲黑人一级黄片| 久久久久性生活片| 午夜日本视频在线| 黄色视频在线播放观看不卡| 国产精品一区二区在线观看99| 欧美zozozo另类| 免费看光身美女| 我要看日韩黄色一级片| 黄色欧美视频在线观看| 亚洲av成人精品一二三区| 一级黄片播放器| 大陆偷拍与自拍| 欧美日韩视频高清一区二区三区二| 在线观看一区二区三区激情| 成人黄色视频免费在线看| 丝袜脚勾引网站| 精品国产一区二区三区久久久樱花 | 精品久久久久久久久亚洲| 国产 精品1| 一个人看的www免费观看视频| 女人十人毛片免费观看3o分钟| 久久久精品免费免费高清| 七月丁香在线播放| 国产精品久久久久久精品古装| 边亲边吃奶的免费视频| 好男人视频免费观看在线| 熟女av电影| 国产成人精品婷婷| 极品少妇高潮喷水抽搐| 精品人妻视频免费看| 一本一本综合久久| 伦理电影大哥的女人| 成人漫画全彩无遮挡| 美女中出高潮动态图| 亚洲国产精品专区欧美| 国产亚洲一区二区精品| 国产成人精品久久久久久| 在线观看免费高清a一片| 国产毛片在线视频| 免费久久久久久久精品成人欧美视频 | 中国国产av一级| 国产成人a区在线观看| 亚洲美女视频黄频| 韩国高清视频一区二区三区| 在线看a的网站| 最近手机中文字幕大全| 看免费成人av毛片| 深爱激情五月婷婷| 久久这里有精品视频免费| 高清日韩中文字幕在线| 亚洲av在线观看美女高潮| 色吧在线观看| 一级爰片在线观看| 精品久久国产蜜桃| av专区在线播放| 噜噜噜噜噜久久久久久91| 国产精品不卡视频一区二区| 一级二级三级毛片免费看| 另类亚洲欧美激情| 日韩av免费高清视频| a级一级毛片免费在线观看| 又粗又硬又长又爽又黄的视频| 精品亚洲成a人片在线观看 | 99热6这里只有精品| 我的老师免费观看完整版| 亚洲综合精品二区| 国产精品久久久久久久电影| 热99国产精品久久久久久7| av网站免费在线观看视频| 少妇 在线观看| 亚洲精品,欧美精品| 晚上一个人看的免费电影| 91aial.com中文字幕在线观看| 91精品国产九色| 男女下面进入的视频免费午夜| 久久精品国产鲁丝片午夜精品| 久久久成人免费电影| 涩涩av久久男人的天堂| 欧美精品国产亚洲| 在线观看免费视频网站a站| 伦理电影免费视频| 国产精品99久久久久久久久| 校园人妻丝袜中文字幕| 大香蕉久久网| 一区二区三区免费毛片| 肉色欧美久久久久久久蜜桃| 亚洲欧美中文字幕日韩二区| 亚洲精品乱码久久久久久按摩| 日本黄色片子视频| 在线观看一区二区三区| 国产精品蜜桃在线观看| 五月伊人婷婷丁香| 极品教师在线视频| 五月伊人婷婷丁香| 欧美 日韩 精品 国产| 国产 一区精品| 国产精品久久久久久精品古装| 91aial.com中文字幕在线观看| 中文字幕亚洲精品专区| 色吧在线观看| 国产精品国产av在线观看| 久久6这里有精品| 三级国产精品欧美在线观看| 国产乱来视频区| 亚洲美女视频黄频| 免费人成在线观看视频色| 午夜免费鲁丝| 亚洲国产高清在线一区二区三| 精品人妻熟女av久视频| 黄片无遮挡物在线观看| 伊人久久国产一区二区| 国产精品三级大全| 欧美激情极品国产一区二区三区 | 成人美女网站在线观看视频| 欧美少妇被猛烈插入视频| 人人妻人人爽人人添夜夜欢视频 | 深爱激情五月婷婷| 亚洲av二区三区四区| 精品人妻熟女av久视频| 只有这里有精品99| 国产在线一区二区三区精| 精品久久国产蜜桃| 日韩国内少妇激情av| 性色avwww在线观看| 亚洲av欧美aⅴ国产| 如何舔出高潮| 亚洲av福利一区|